Efficient, Zero Scrambling Fragmentation of Deuterium Labeled Peptides on the ZenoToF 7600 Electron Activated Dissociation Platform
Bibliographic record
Abstract
Hydrogen-deuterium exchange (HDX) mass spectrometry (MS) has become an increasingly important tool in protein research, with large-scale applications in biopharmaceutical development and manufacturing. One of the limitations of classical bottom-up HDX is that it usually provides a "peptide-averaged" picture of structure and dynamics, rather than site-specific (i.e., individual amino acid-level) information. A major challenge for site-specific HDX-MS analyses has been that classical fragmentation techniques such as CAD invariably cause random redistribution of the deuterium labels across the peptide backbone, known as deuterium scrambling. Several groups have demonstrated that this problem can be overcome using nonergodic fragmentation and "cool" ion flight conditions. A major hurdle to widespread adoption of this approach, however, is that the exceedingly low fragmentation efficiency of electron capture dissociation (ECD) combined with the lower transmission efficiency of "cool" ion flight conditions impose a very strong attenuation on sensitivity, to the point where this method becomes impractical for many "real-world" applications. Here, we introduce a workflow and instrument conditions on the Sciex 7600 ZenoToF electron activated dissociation (EAD) platform that allow for zero scrambling ECD fragmentation with limited (and in some cases no) sensitivity loss. We expect that this workflow will be ideal for broadly applicable, site-specific HDX-MS analyses using a middle-down workflow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".